1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
27/2/2025 Comida 10000 Andrés NA
26/2/2025 Comida 4620 Andrés NA
1/3/2025 Comida 2300 Tami Supermercado
2/3/2025 Comida 102058 Tami Supermercado
3/3/2025 Comida 9370 Andrés NA
9/3/2025 Comida 61916 Tami Supermercado
11/3/2025 Comida 27021 Andrés NA
11/3/2025 Enceres 13190 Tami 40 rollos confort
15/3/2025 Comida 78061 Tami Supermercado
17/3/2025 Electricidad 52458 Andrés NA
17/3/2025 VTR 22000 Andrés NA
21/3/2025 Agua 19562 Andrés NA
22/3/2025 Comida 76766 Tami Supermercado
21/3/2025 Diosi 18500 Andrés antiparasitario
27/3/2025 Gas 82450 Andrés NA
26/3/2025 Comida 4000 Andrés avena multigrano y chucrut
29/3/2025 Comida 70591 Tami Supermercado
3/4/2025 Gas 83300 Andrés NA
4/4/2025 Agua 20807 Andrés NA
6/4/2025 Comida 52655 Tami Supermercado
12/4/2025 Comida 72108 Tami Supermercado
16/4/2025 VTR 21990 Andrés NA
22/4/2025 Comida 107881 Tami Supermercado
26/4/2025 Comida 55874 Tami Supermercado
28/4/2025 Comida 13050 Tami Cervezas MUT
29/4/2025 Electricidad 52507 Andrés enel
29/4/2025 Diosi 11990 Andrés arena 7kg superzoo
3/5/2025 Agua 17072 Andrés aguas andina
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 1.0039e+09   2    5.1491  0.006 ** 
## lag_depvar    2.6287e+11   1 2696.4694 <2e-16 ***
## Residuals     8.1401e+10 835                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1825.235 16282.91 0.1467164
## 2-0 31393.306 23245.767 39540.84 0.0000000
## 2-1 24164.467 19448.396 28880.54 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
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## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   683 53627.57 22091.176
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2018.75467   4040.31561   -537.98099   2438.15035  -2969.45204    518.81136 
##            8            9           10           11           12           13 
##  -5655.75231  -1187.80602  -3966.13252   -417.98090  -4939.71464  -1609.41835 
##           14           15           16           17           18           19 
##   -899.74433    377.51733  -3242.78287   -377.80026  -2129.96567   6604.28688 
##           20           21           22           23           24           25 
##  -1529.15181  -1208.27771   1475.61382  -1186.61211    234.63187   1695.00269 
##           26           27           28           29           30           31 
##  -7102.01296    947.67561   8192.64621    418.64068    -13.46253  -2399.97936 
##           32           33           34           35           36           37 
##   1576.85052   4573.21941   1127.72494   2392.32323  -1867.04906   4608.91124 
##           38           39           40           41           42           43 
##   4304.33764  -2274.84395  -2980.65631  -1109.44993 -10740.83104   7289.85511 
##           44           45           46           47           48           49 
##   2557.70098   1367.24294   8105.52382    686.51573   6529.10508   6714.75277 
##           50           51           52           53           54           55 
##  -5881.95985  -4795.09396  -5059.40802  -7928.27117   6130.07123  -4076.36759 
##           56           57           58           59           60           61 
##  -4894.12288   3855.96615    887.96062    -32.01797    142.29883  -4996.37715 
##           62           63           64           65           66           67 
##  18126.81488   3640.73409  -3645.99846   5925.51951   7344.28641  14639.24755 
##           68           69           70           71           72           73 
##   1693.72801 -13212.08254  -1305.38956   4644.31455  -4899.47278  -4403.67327 
##           74           75           76           77           78           79 
## -10496.51067   2468.06961  -5398.49563   1065.17873  -6864.88022    548.46541 
##           80           81           82           83           84           85 
##  -2353.07872  -2692.33976  -3930.30708   -535.86697   2316.21397   3764.07304 
##           86           87           88           89           90           91 
##    477.99046   -483.64422    197.38841   4302.58742  -1162.60983   1151.23655 
##           92           93           94           95           96           97 
##  -2064.17145  -1043.95149    177.93113    275.09092  -7483.77048   2392.38795 
##           98           99          100          101          102          103 
##  -8601.95670  -2939.49538  -4038.09771  -1735.01589  -1259.44550   3182.93994 
##          104          105          106          107          108          109 
##  -2340.35658   2595.73115  -1156.98295    971.83180   2588.29057  -3153.44912 
##          110          111          112          113          114          115 
##  -4722.00512   -848.99326   1904.77747  11694.61150  -1242.27644   2668.91494 
##          116          117          118          119          120          121 
##   4263.10248   3502.77168  -1099.93955  -4716.15751  -3723.56998   2320.55845 
##          122          123          124          125          126          127 
##  -1731.96902   1341.22908   8858.98782    847.38598    130.75792  -2520.89734 
##          128          129          130          131          132          133 
##   2655.62788   7052.97400   1012.56544  -8499.25545   1749.85118   4136.05801 
##          134          135          136          137          138          139 
##  -3163.62971  -1419.21432   -853.37593  -3879.35766   1183.90979   -494.70566 
##          140          141          142          143          144          145 
##  -2912.82821   1719.07605  -1880.31347  -7828.46824   2040.68678  -3478.70908 
##          146          147          148          149          150          151 
##   2103.32513   -256.64054   1023.76172   -358.73086   1352.70356   1186.99076 
##          152          153          154          155          156          157 
##   3356.81599  -4861.77417  -1174.15951  -3235.44434   5957.25293   9746.77867 
##          158          159          160          161          162          163 
##  -3652.36440  -4998.47556   3384.31224    -25.19707   2476.05482  -6131.26589 
##          164          165          166          167          168          169 
##  -6967.69248   3937.10200  17171.63989   3396.35955   -631.92750  -2679.21475 
##          170          171          172          173          174          175 
##  -1336.29253   3358.70438   -461.90588  -8309.11008   2633.45764   4093.30901 
##          176          177          178          179          180          181 
##    390.87965   8515.24838  -9488.21908  -3706.66945 -10978.11279 -11468.89484 
##          182          183          184          185          186          187 
##   1010.54606   9066.84607  -1662.66176   5695.78790   6317.49745  12914.20325 
##          188          189          190          191          192          193 
##   8175.10149  -4327.35032   2201.46145  10101.53572  -1920.38325  -2720.55251 
##          194          195          196          197          198          199 
## -10554.76186  -6628.92860    973.32165  -5494.29877 -10052.12845   5136.20667 
##          200          201          202          203          204          205 
##  -3319.78828  -1961.03385  -1051.31064   6247.47532   9626.94520    310.81447 
##          206          207          208          209          210          211 
##   2655.75889   2825.81699   5509.33845  12553.49040  -5979.13573 -11579.64514 
##          212          213          214          215          216          217 
##  -5936.26202 -10850.02323  -5325.98851   1281.57059 -13257.25235  16155.82785 
##          218          219          220          221          222          223 
##   7557.04996   1266.29766  26424.24441  12231.09080   7026.78051  13712.89896 
##          224          225          226          227          228          229 
##  -4239.64254  -2057.73862   3467.22019     50.56659   2441.83056   8703.18097 
##          230          231          232          233          234          235 
##   5526.33708  -2208.23680  -2121.74244   9138.73246 -11801.32400  -7560.77688 
##          236          237          238          239          240          241 
##  -8808.97181 -10359.42183   2830.30995   1101.32497  -8549.25401  -9232.95758 
##          242          243          244          245          246          247 
##   8860.10231  -8011.42160   2248.84414 -10543.85073  -4286.64131   1192.20281 
##          248          249          250          251          252          253 
##    768.30212 -12554.22101   3416.72061   1828.30377   3972.21910   1887.47355 
##          254          255          256          257          258          259 
##  -1412.58044  10886.72058  20611.86367   2897.27024  -4575.15008   3812.64190 
##          260          261          262          263          264          265 
##  -1995.81403   3440.04968  -5152.27433 -11185.50518  -5004.22091   -790.57433 
##          266          267          268          269          270          271 
##  -5456.76489   8516.05687  -4557.22083   3917.76480  -2386.31494   4153.72320 
##          272          273          274          275          276          277 
##    423.89686   7016.06950  -1711.45788  11727.93193  -4902.60296   1415.00961 
##          278          279          280          281          282          283 
##   -684.94799   7540.51905  -5381.08149  -3042.95504 -11565.41076  -2949.60283 
##          284          285          286          287          288          289 
##  18380.31938   7472.89750   2409.71526   -956.01739    582.51782   6075.44990 
##          290          291          292          293          294          295 
##   6549.32249 -19115.90566 -11436.67398  -8391.20106   9414.86351   2801.10478 
##          296          297          298          299          300          301 
##  -1456.06848  27128.41620   9730.02582   4546.95789   9159.08060   2482.96677 
##          302          303          304          305          306          307 
##  -1404.01291   7535.93746 -24666.03098  -3836.59125   -463.04985  -7251.28986 
##          308          309          310          311          312          313 
##  -4233.83544   2682.49910  -9447.75516  -3460.34750  -8407.58931   1364.96266 
##          314          315          316          317          318          319 
##  -3357.98332   1846.94659  -4293.08092  27241.58805  -1024.61667   2993.50789 
##          320          321          322          323          324          325 
##  10525.09440   5257.15611  32038.74095   4691.40589 -21353.71471   1448.04938 
##          326          327          328          329          330          331 
##    770.17445  -6799.74060  -2043.04646 -33565.45094    717.55340  -2468.10862 
##          332          333          334          335          336          337 
##   -252.06981  -3327.35878   3933.74389   -603.93214  -7120.18690  -3265.43467 
##          338          339          340          341          342          343 
##  -2334.58653  -7819.86739   3730.04957  -1511.55536  -1879.33106  -1135.29680 
##          344          345          346          347          348          349 
##     33.02657    332.20031  -1774.70380  -9602.84985 -13342.50075   2211.20578 
##          350          351          352          353          354          355 
##  -4439.16512  -3769.06466  -6087.54695   1652.98026   1270.18552   2624.30721 
##          356          357          358          359          360          361 
##  -3914.00106   -659.65402    527.61836   6854.12793     86.58033   -233.34283 
##          362          363          364          365          366          367 
##   2384.63506  -2960.25236  -1078.64904  -8942.42867  -4794.04251  -6366.26031 
##          368          369          370          371          372          373 
##  -5084.73974  -7375.50047   4910.95361    238.48000   6977.71940  -7811.59801 
##          374          375          376          377          378          379 
##  -2413.96014  -3535.36135  -2607.00546 -12593.90191   1805.37502 -10748.50740 
##          380          381          382          383          384          385 
##   5611.42792   9218.02901   2962.85737  -2580.42847   1426.81456   6554.98944 
##          386          387          388          389          390          391 
##  11191.19158  -6069.08800  -5610.53333   -387.33568   8331.91683   1548.83098 
##          392          393          394          395          396          397 
##  10948.83655 -10194.38567   2497.58796    426.56470    275.75755   -940.05998 
##          398          399          400          401          402          403 
##   -844.12714 -14763.53169   8311.23106  -1421.83177  -1605.31696   6756.83173 
##          404          405          406          407          408          409 
##  -8183.94356  -1512.26324  -2738.73510  -6013.36885  -3027.59087  -4074.29790 
##          410          411          412          413          414          415 
##  -8898.31090   6022.29055   1496.58167  -7529.84309  -7823.03945  14115.25430 
##          416          417          418          419          420          421 
##   3639.49776   4291.41644  -8260.46515  -4935.80931  -2773.45470   2656.77885 
##          422          423          424          425          426          427 
## -14188.60442  -2913.36774  -9214.90029   2927.61276   6868.95541   6428.21611 
##          428          429          430          431          432          433 
##  -4169.38173  -4289.32417  -4876.90594  -1928.92529  -5849.21431  -6746.40775 
##          434          435          436          437          438          439 
##  -6049.75917  -1478.46408   -938.01097  -5072.37920   2494.47674   4730.46273 
##          440          441          442          443          444          445 
##  -5195.28262  -2282.97433   1452.85564  -3974.71440   2707.16515  -6725.15551 
##          446          447          448          449          450          451 
## -12236.38657  -4596.09124   9568.17482  -2157.67593   4630.33636  -6018.74073 
##          452          453          454          455          456          457 
##  -1252.90944    252.44049   2887.88024 -12423.61649   3257.45078  -6833.13882 
##          458          459          460          461          462          463 
##   6410.37847   2868.11199   2346.71337  -4018.86104   1932.57811   -178.39774 
##          464          465          466          467          468          469 
##   1619.99071   -702.86842   3169.72484  -2833.74851   5621.58045  -7147.56665 
##          470          471          472          473          474          475 
##  -3139.87726  -2368.44668  -4818.47344   2858.48891   7644.89892  -6201.34500 
##          476          477          478          479          480          481 
##   1323.72706  -6345.88576  -2988.41207   1876.36181 -13076.25639  -9857.05842 
##          482          483          484          485          486          487 
##  -1276.09976    -59.01332  -1051.02394  -1435.86196  -9682.09939  11024.15928 
##          488          489          490          491          492          493 
##   6113.61024   7270.10216  -5617.13000   5206.31398   9110.38174   5837.02119 
##          494          495          496          497          498          499 
## -13709.58582 -10746.98013  -3582.17175  -1236.37850   -653.99173  -7757.02895 
##          500          501          502          503          504          505 
##    504.67481   4173.95355   5374.66939    503.27497    -80.10092  -7400.70640 
##          506          507          508          509          510          511 
##    433.81077  -5189.11618   1707.54745  -1431.68992  -8291.39478   -709.87736 
##          512          513          514          515          516          517 
##  -2785.68452   -694.79848   1221.43250  -9616.18789  -7857.69011  24213.47839 
##          518          519          520          521          522          523 
##   9656.86758   5674.89233  -5559.02900   2597.86975  16810.58500  11206.57620 
##          524          525          526          527          528          529 
## -24449.51819  -5262.85234  -3915.21889   4404.70998   -541.08372 -11284.94332 
##          530          531          532          533          534          535 
##   4249.79618  13749.64286  -5188.11104   4182.67314   5348.88235  -2014.54774 
##          536          537          538          539          540          541 
##  -4754.73984  -7267.89628  -2266.40659   8164.49712    -61.34187  -8328.81098 
##          542          543          544          545          546          547 
##   1659.53504   -763.36183    204.32230 -11194.62788 -11186.38707   1951.36933 
##          548          549          550          551          552          553 
##   6899.63416  -1452.43455    703.53219  -7860.41648   8449.86042    756.08842 
##          554          555          556          557          558          559 
## -12100.22281   9046.70246   8503.31707    -89.40487   4664.46143  -3780.15716 
##          560          561          562          563          564          565 
##  13917.32772  21257.16507  -6781.21640  -9960.68706   6538.98851    -29.00266 
##          566          567          568          569          570          571 
##   3205.72417  -7631.77827 -17527.52987   6512.57681   6261.36330   1708.08229 
##          572          573          574          575          576          577 
##   2900.87837   1565.30572  -2371.54491  14522.64528  -9893.47130  -6448.70134 
##          578          579          580          581          582          583 
##   8532.59356   2649.99433  -6760.25399   7321.93305  -4011.16127  -2968.97970 
##          584          585          586          587          588          589 
##  15522.10348 -14733.76531   8250.36601   -135.53386  -6414.16668   -929.55133 
##          590          591          592          593          594          595 
##     81.54040 -10822.74163   1666.46178  -7282.81088   2953.09361   8737.62569 
##          596          597          598          599          600          601 
##  -7662.93716   5715.36355   2576.27219   6687.34462  -3381.74502   5972.05450 
##          602          603          604          605          606          607 
##  -8495.26033   2091.23644   1099.28317   2964.95868   1313.11866    213.15474 
##          608          609          610          611          612          613 
##  -5992.87412   7915.62202  -1369.23661  -2751.64736  -3618.53106  -8379.17598 
##          614          615          616          617          618          619 
##  11836.80407   4772.61760  -9492.89024  11482.60027   5867.66153  -5771.31456 
##          620          621          622          623          624          625 
##  26186.17562 -13079.85897  -6978.43071   2987.77474  -4335.14284 -10750.41317 
##          626          627          628          629          630          631 
##  11174.86447 -21794.40725  -2503.94799   8589.15503  11017.53717  -1707.18905 
##          632          633          634          635          636          637 
##  33136.30855  -6836.92425   5499.53346   5172.32085  -2502.53897  -5561.62883 
##          638          639          640          641          642          643 
##  -2131.25290 -12610.11662  -2378.78367  -2015.39865  -2644.02608  -2974.90284 
##          644          645          646          647          648          649 
##   1705.27141   4313.23469  16831.98453  18368.05344    645.90788   4558.09415 
##          650          651          652          653          654          655 
##  10375.31121  19892.43242    443.25634 -28346.45198  -1523.56283  -2460.94721 
##          656          657          658          659          660          661 
##   1712.83962  -3346.26619 -10760.90044   1558.99635   4116.57727  -1127.36812 
##          662          663          664          665          666          667 
##  12915.84384   1210.20656   1663.97527 -11841.07486   1263.62065   1069.50486 
##          668          669          670          671          672          673 
##  -5285.14604  -7513.93528   1982.46335  -3802.15894   2591.25598  -3469.54350 
##          674          675          676          677          678          679 
##  -9420.23135  -8370.61134  -3029.70114    119.65274   2786.60316    640.60990 
##          680          681          682          683          684          685 
##  -3904.84616  -1881.70450  -1391.58439  -8316.92637   4587.98201  -2318.78042 
##          686          687          688          689          690          691 
##  -1473.49089    511.43600  10772.72291   9742.75492  10496.82748  -9807.16910 
##          692          693          694          695          696          697 
##  -3670.53436  -3245.85981   5772.81504 -10494.94797  -7997.49349  -8682.19387 
##          698          699          700          701          702          703 
##  -6331.63716  -4789.67567   3034.40892  -4461.73256  -1954.75225   4163.94841 
##          704          705          706          707          708          709 
##  31036.22131   9417.28970  23343.28609   1577.04363   8229.83961  22833.30335 
##          710          711          712          713          714          715 
##   6476.17504 -18278.37609   4764.60479  -5497.95910   -150.10226    431.82496 
##          716          717          718          719          720          721 
## -17313.64135  -5305.41191   3295.12682  -3054.34438 -13018.35587   4243.63081 
##          722          723          724          725          726          727 
##  -5594.37700    705.49921  -3972.60192 -12484.55783   1333.47426  -1902.77246 
##          728          729          730          731          732          733 
##  -9813.63378  17234.94012   1734.53842  -2764.34516   5674.33500  -8673.41772 
##          734          735          736          737          738          739 
##   -763.43224   8098.21235 -15394.46241  -5949.06426   7371.28167  -4824.62440 
##          740          741          742          743          744          745 
##    121.78468   1788.03440  -1996.61457  -5208.52372   6373.42078  -6316.73003 
##          746          747          748          749          750          751 
##  22658.78002   7780.42918  -1994.93831  -7334.57724  23372.95316  -4339.40870 
##          752          753          754          755          756          757 
##   1351.14608 -14466.33800  56069.18759  26875.51049  15053.05479 -10683.91146 
##          758          759          760          761          762          763 
##  10574.88833   7284.50865   5781.41787 -46415.42587 -16191.72487    946.78964 
##          764          765          766          767          768          769 
##  -2537.39347  -3477.70911 122824.12832  19301.62583  43713.97860  22585.22539 
##          770          771          772          773          774          775 
##  12078.47625  15850.14183  25730.98864 -98803.58752  -6765.82116 -35841.92504 
##          776          777          778          779          780          781 
##   1721.94675  -1243.79836   3380.96834  -7429.33726  -1460.37252  -1960.59713 
##          782          783          784          785          786          787 
##   3409.10789  -7169.69040  -2251.85469   3904.46418   2251.43403  -2772.11251 
##          788          789          790          791          792          793 
##  -4060.28681   1725.98076   2841.27462    -62.62740  -6714.16835  -5793.85350 
##          794          795          796          797          798          799 
##  -1159.21320  -1274.89507  -7828.96619  -2366.53520  -3280.87478  -2678.46832 
##          800          801          802          803          804          805 
##  10711.31736   2230.10678   7069.17396   2914.22743  -5456.83940   8178.44879 
##          806          807          808          809          810          811 
##   9867.00637 -10608.31379  -7403.57475  -7512.87711   2997.60546   4186.58529 
##          812          813          814          815          816          817 
##  -2276.56254 -14171.97130  -4118.85781   6251.05116   8229.49139  -9679.66579 
##          818          819          820          821          822          823 
##  -7757.14951  -9343.77577   9745.74793  -1230.10737  -4378.90981  -8454.30147 
##          824          825          826          827          828          829 
##   7866.77759   7906.91824   4969.03998  -3108.51927   -718.28784   2885.51476 
##          830          831          832          833          834          835 
##   4662.11434   1617.99859  -6700.45038   2081.88718   -405.28593   2746.20424 
##          836          837          838          839          840 
##   4133.38883  -1143.76636  -9342.25633   5668.96284  -4868.44602 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17250.53  20098.68  24354.12  24071.99  26426.17  23757.90  24474.47  19704.95 
##        10        11        12        13        14        15        16        17 
##  19441.42  16783.27  17561.00  14289.28  14340.46  15005.34  16702.50  15021.94 
##        18        19        20        21        22        23        24        25 
##  16056.97  15430.28  22515.15  21598.85  21078.53  22969.18  22294.94  22947.71 
##        26        27        28        29        30        31        32        33 
##  24794.30  18720.61  20447.35  28287.36  28345.03  28017.84  25646.44  27049.35 
##        34        35        36        37        38        39        40        41 
##  30893.70  31242.25  32651.91  30161.66  34138.66  37347.84  34402.94  31212.74 
##        42        43        44        45        46        47        48        49 
##  30060.12  20636.43  28157.73  30595.04  31684.62  38525.06  38019.47  42683.25 
##        50        51        52        53        54        55        56        57 
##  46920.96  39616.38  34182.98  29203.99  22346.07  28638.22  25217.69  21514.03 
##        58        59        60        61        62        63        64        65 
##  25923.90  27183.88  27480.99  27892.95  23762.47  40359.41  42204.00  37448.34 
##        66        67        68        69        70        71        72        73 
##  41656.71  46574.04  57245.84  55258.94  40497.10  38002.11  41021.04  35319.24 
##        74        75        76        77        78        79        80        81 
##  30769.94  21470.22  24672.78  20597.11  22683.88  17577.68  19593.79  18820.05 
##        82        83        84        85        86        87        88        89 
##  17847.45  15915.72  17193.93  20803.21  25222.44  26212.64  26237.61  26854.56 
##        90        91        92        93        94        95        96        97 
##  30981.04  29811.19  30810.89  28874.67  28074.21  28442.48  28849.20  22424.47 
##        98        99       100       101       102       103       104       105 
##  25440.53  18468.64  17324.38  15364.44  15664.30  16341.92  20816.07  19899.27 
##       106       107       108       109       110       111       112       113 
##  23411.55  23201.45  24878.14  27755.88  25253.15  21695.42  21970.94  24618.10 
##       114       115       116       117       118       119       120       121 
##  35486.28  33678.51  35516.61  38515.94  40472.51  38160.16  32979.43  29319.58 
##       122       123       124       125       126       127       128       129 
##  31403.11  29682.49  30864.44  38466.76  38109.10  37170.33  34032.80  35814.60 
##       130       131       132       133       134       135       136       137 
##  41214.29  40654.40  31853.15  33118.37  36309.20  32718.64  31105.38  30190.07 
##       138       139       140       141       142       143       144       145 
##  26745.95  28160.85  27930.40  25615.92  27641.03  26265.33  19865.31  22896.85 
##       146       147       148       149       150       151       152       153 
##  20722.82  23700.93  24241.10  25832.02  26014.15  27668.87  28970.04  32003.20 
##       154       155       156       157       158       159       160       161 
##  27471.87  26734.59  24289.03  30185.08  41672.79  40002.48  37366.54  42388.48 
##       162       163       164       165       166       167       168       169 
##  43797.52  47214.55  42678.98  37984.61  43411.65  59719.21  61932.07  60345.64 
##       170       171       172       173       174       175       176       177 
##  57170.29  55569.01  58272.48  57296.25  49585.83  52410.26  56154.12  56190.32 
##       178       179       180       181       182       183       184       185 
##  63321.50  53820.67  50570.54  41376.18  32912.74  36422.15  46528.95  45984.78 
##       186       187       188       189       190       191       192       193 
##  51939.50  57686.37  68472.90  73757.49  67450.11  67643.61  74716.24  70391.27 
##       194       195       196       197       198       199       200       201 
##  65912.62  55152.93  49181.11  50605.87  46199.13  38365.36  44792.22  43019.03 
##       202       203       204       205       206       207       208       209 
##  42656.88  43135.38  49931.63  58823.76  58453.24  60178.61  61834.95  65627.37 
##       210       211       212       213       214       215       216       217 
##  75096.99  67177.22  55362.40  49969.45  40962.85  37919.57  41034.25  31051.17 
##       218       219       220       221       222       223       224       225 
##  48030.24  55353.42  56255.61  79028.48  86525.93  88529.82  96123.64  87071.60 
##       226       227       228       229       230       231       232       233 
##  81068.07  80649.86  77298.74  76459.96  81198.52  82563.24  76996.89  72208.27 
##       234       235       236       237       238       239       240       241 
##  77863.75  64507.21  56541.11  48489.14  40097.98  44291.25  46444.68  39893.24 
##       242       243       244       245       246       247       248       249 
##  33570.75  43856.56  38101.58  42038.57  34299.93  33005.37  36661.84  39486.65 
##       250       251       252       253       254       255       256       257 
##  30313.14  36253.12  40055.78  45252.24  47971.44  47463.85  57768.14  75271.02 
##       258       259       260       261       262       263       264       265 
##  75086.01  68394.50  69876.81  66096.38  67542.99  61298.65  50569.79  46595.86 
##       266       267       268       269       270       271       272       273 
##  46805.34  42910.80  51717.79  47989.66  52137.74  50253.71  54322.39  54618.50 
##       274       275       276       277       278       279       280       281 
##  60637.89  58271.35  67947.46  61870.28  62080.38  60428.91  66173.65  59902.10 
##       282       283       284       285       286       287       288       289 
##  56464.84  46013.75  44409.97  61647.82  67179.71  67589.30  65006.05  64093.12 
##       290       291       292       293       294       295       296       297 
##  68095.39  72006.91  52997.25  43096.06  37105.14  47429.90  50672.78  49786.44 
##       298       299       300       301       302       303       304       305 
##  73990.69  79938.04  80605.92  85219.89  83417.87  78446.49  81914.46  56805.02 
##       306       307       308       309       310       311       312       313 
##  53064.91  52744.58  46532.69  43741.22  47345.76  39895.49  38617.16  33176.89 
##       314       315       316       317       318       319       320       321 
##  36962.70  36143.77  39976.51  37960.27  63755.19  61595.63  63219.76  71220.56 
##       322       323       324       325       326       327       328       329 
##  73608.69  99098.88  97476.00  73298.09  72095.54  70452.31  62401.33  59522.59 
##       330       331       332       333       334       335       336       337 
##  29460.88  33149.68  33589.36  35910.07  35250.68  41019.65  42095.62  37341.58 
##       338       339       340       341       342       343       344       345 
##  36555.73  36682.44  31999.81  38000.84  38664.47  38923.01  39799.12  41585.66 
##       346       347       348       349       350       351       352       353 
##  43408.28  43159.85  36102.07  26666.65  32013.17  30873.78  30463.69  28079.31 
##       354       355       356       357       358       359       360       361 
##  32759.81  36515.41  40980.57  39168.94  40429.67  42568.87  49966.71  50517.49 
##       362       363       364       365       366       367       368       369 
##  50719.22  53183.25  50665.79  50110.14  42752.76  39948.55  36124.17  33902.07 
##       370       371       372       373       374       375       376       377 
##  29958.47  37248.95  39536.71  47425.03  41394.53  40841.50  39378.29  38910.90 
##       378       379       380       381       382       383       384       385 
##  29775.34  34375.08  27424.29  35646.54  45983.29  49550.00  47822.76  49815.15 
##       386       387       388       389       390       391       392       393 
##  56037.52  65526.37  58735.25  53201.48  52930.08  60312.31  60835.88  69507.67 
##       394       395       396       397       398       399       400       401 
##  58609.41  60176.86  59736.81  59220.49  57706.84  56467.96  43221.77  51810.55 
##       402       403       404       405       406       407       408       409 
##  50810.60  49776.45  56180.09  48719.83  48030.74  46356.80  42032.45  40862.73 
##       410       411       412       413       414       415       416       417 
##  38925.88  33017.85  40893.56  43820.99  38491.33  33577.75  48454.93  52301.15 
##       418       419       420       421       422       423       424       425 
##  56231.89  48698.24  45020.17  43695.65  47283.46  35698.22  35427.33  29683.96 
##       426       427       428       429       430       431       432       433 
##  35275.90  43606.64  50501.38  47265.61  44333.19  41257.21  41145.36  37621.84 
##       434       435       436       437       438       439       440       441 
##  33758.76  30991.75  32568.44  34418.52  32422.38  37290.39  43498.28  40249.40 
##       442       443       444       445       446       447       448       449 
##  39955.29  42962.86  40848.12  44839.16  40084.24  31113.09  29950.11  41311.39 
##       450       451       452       453       454       455       456       457 
##  40992.81  46646.17  42280.62  42630.42  44251.55  47971.19  37841.55  42692.71 
##       458       459       460       461       462       463       464       465 
##  38114.19  45686.17  49207.57  51829.15  48557.42  50899.11  51100.72  52848.44 
##       466       467       468       469       470       471       472       473 
##  52345.85  55290.75  52617.99  57671.14  50928.45  48538.45  47124.04  43747.08 
##       474       475       476       477       478       479       480       481 
##  47504.67  54970.92  49395.70  51099.60  45886.41  44264.78  47098.83  36508.92 
##       482       483       484       485       486       487       488       489 
##  30067.96  31938.01  34635.74  36126.29  37092.53  30730.84  43265.96  49928.75 
##       490       491       492       493       494       495       496       497 
##  56761.70  51471.11  56306.05  63942.69  67755.59  54006.55  44580.74  42604.95 
##       498       499       500       501       502       503       504       505 
##  42928.28  43719.74  38204.33  40604.19  45907.76  51591.58  52301.53  52412.13 
##       506       507       508       509       510       511       512       513 
##  46111.62  47452.12  43709.88  46466.40  46131.97  39845.31  40976.83  40151.66 
##       514       515       516       517       518       519       520       521 
##  41257.71  43898.76  36736.12  32013.66  55912.56  64076.39  67730.74  61107.27 
##       522       523       524       525       526       527       528       529 
##  62447.27  76038.14  83017.52  57958.14  52826.22  49519.29  53899.94  53406.09 
##       530       531       532       533       534       535       536       537 
##  43585.92  48579.64  61244.97  55763.76  59162.69  63151.98  60203.45  55232.32 
##       538       539       540       541       542       543       544       545 
##  48692.12  47347.50  55287.63  55037.95  47595.18  49819.65  49646.25  50340.34 
##       546       547       548       549       550       551       552       553 
##  40985.82  32818.49  37161.94  45281.58  45078.47  46784.99  40792.57  49808.91 
##       554       555       556       557       558       559       560       561 
##  50964.65  40740.01  50284.54  58150.26  57514.97  61114.01  56879.67  68644.55 
##       562       563       564       565       566       567       568       569 
##  85339.36  75426.69  63986.01  68406.86  66530.56  67717.64  59284.53  43267.71 
##       570       571       572       573       574       575       576       577 
##  50278.92  56186.20  57369.41  59445.69  60092.97  57218.35  69469.47  58838.99 
##       578       579       580       581       582       583       584       585 
##  52559.69  60164.01  61668.54  54760.07  61028.88  56603.41  53646.90  67221.91 
##       586       587       588       589       590       591       592       593 
##  52645.21  59992.11  59084.17  52804.12  52109.03  52385.17  43097.68  45895.53 
##       594       595       596       597       598       599       600       601 
##  40520.05  44767.37  53533.79  46862.64  52723.73  55102.37  60773.46  56930.23 
##       602       603       604       605       606       607       608       609 
##  61745.69  53311.33  55192.00  55968.61  58277.60  58851.85  58392.45  52567.81 
##       610       611       612       613       614       615       616       617 
##  59631.95  57691.36  54787.53  51492.46  44452.91  55967.24  59856.03  50788.26 
##       618       619       620       621       622       623       624       625 
##  61193.91  65380.31  58867.82  81103.14  66220.72  58547.37  60551.00  55902.70 
##       626       627       628       629       630       631       632       633 
##  46234.71  56945.84  37495.38  37355.56  46927.18  57413.47  55457.41  84196.35 
##       634       635       636       637       638       639       640       641 
##  74379.18  76580.68  78218.54  72943.06  65659.82  62292.97  50193.78  48561.54 
##       642       643       644       645       646       647       648       649 
##  47452.74  45934.47  44318.59  46996.34  51615.30  66591.23  81020.38  78142.76 
##       650       651       652       653       654       655       656       657 
##  79046.83  84920.28  98369.46  93126.31  63386.42  60837.38  57790.73  58775.69 
##       658       659       660       661       662       663       664       665 
##  55215.47  45625.00  48010.14  52329.37  51521.30  63086.94  62964.60  63254.22 
##       666       667       668       669       670       671       672       673 
##  51705.81  53065.78  54084.57  49421.79  43399.54  46435.44  44033.46  47521.40 
##       674       675       676       677       678       679       680       681 
##  45273.09  38108.33  32764.56  32762.06  35511.97  40245.53  42506.70  40510.56 
##       682       683       684       685       686       687       688       689 
##  40534.16  40983.07  35323.59  41655.07  41152.35  41451.71  43447.85  54159.10 
##       690       691       692       693       694       695       696       697 
##  62619.17  70671.03  59964.39  55970.86  52852.18  58007.95  48297.64  41994.62 
##       698       699       700       701       702       703       704       705 
##  35888.35  32606.39  31085.88  36594.30  34857.32  35530.19  41465.06  70133.85 
##       706       707       708       709       710       711       712       713 
##  76294.43  93847.24  90165.30  92761.41 107791.40 106631.66  83986.25  84333.67 
##       714       715       716       717       718       719       720       721 
##  75669.25  72771.03  70746.93  53471.13  48868.02  52361.20  49865.21  38976.94 
##       722       723       724       725       726       727       728       729 
##  44546.66  40816.79  43062.60  40937.13  31641.53  35593.49  36218.92  29852.49 
##       730       731       732       733       734       735       736       737 
##  47925.75  50174.06  48207.38  53862.99  46267.29  46541.93  54525.75  40973.21 
##       738       739       740       741       742       743       744       745 
##  37384.15  45887.91  42661.50  44164.54  46934.04  46046.95  42465.01  49455.87 
##       746       747       748       749       750       751       752       753 
##  44475.51  65443.86  70765.65  66873.86  58806.90  78591.55  71663.85  70582.77 
##       754       755       756       757       758       759       760       761 
##  55815.81 104549.63 121624.95 126215.20 107735.97 110164.92 109412.15 107440.85 
##       762       763       764       765       766       767       768       769 
##  60105.58  45152.50  47062.25  45686.42  43662.44 152263.66 156701.74 181912.92 
##       770       771       772       773       774       775       776       777 
## 185480.38 179416.43 177413.30 184297.30  81487.39  72074.07  38439.77  41873.66 
##       778       779       780       781       782       783       784       785 
##  42282.75  46681.62  41078.94  41399.03  41241.61  45796.40  40532.28  40229.68 
##       786       787       788       789       790       791       792       793 
##  45344.99  48370.54  46624.57  43973.16  46712.58  50081.06  50487.03  45029.28 
##       794       795       796       797       798       799       800       801 
##  41064.21  41649.32  42059.54  36690.68  36772.45  36044.90  35935.54  47540.75 
##       802       803       804       805       806       807       808       809 
##  50270.68  56884.92  59033.98  53596.84  60760.85  68496.74  57364.29  50436.59 
##       810       811       812       813       814       815       816       817 
##  44287.25  48098.27  52467.56  50637.83  38644.00  36948.09  44527.94  52880.52 
##       818       819       820       821       822       823       824       825 
##  44529.44  38911.78  32616.25  43796.39  43974.91  41379.30  35549.79  44717.94 
##       826       827       828       829       830       831       832       833 
##  52764.67  57229.09  54071.72  53401.34  55964.74  59757.29  60411.31  53713.68 
##       834       835       836       837       838       839       840 
##  55535.43  54953.94  57199.75  60374.48  58537.26  49767.47  55221.59 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8102
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.149055   0.7793924    3.893868
## t2* 2696.469396 169.2176680  897.884015
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.101622       5.160809   13.39701
## 2    lag_depvar 1641.420957    2736.931326 4550.39893

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon May 12 00:59:38 2025
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##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 10.09225 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 312.85525 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.00000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 56.21325 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 3.29750 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.00000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 41.43750 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 26.12225 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.00000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.00000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 16.49500 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.00000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.00000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 466.51300 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2716, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2716 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-06-09 00:04:58 sería de: 26.669 pesos// Percentil 95% más alto proyectado: 35.110,81

Según TimeGPT: La proyección de la UF a 298 días más 2026-04-03 sería de: 40.528,13 pesos// Percentil 80% más alto proyectado: 40.841,38 pesos// Percentil 95% más alto proyectado: 41.156,83

Según prophet: La proyección de la UF a 298 días más 2026-04-03 sería de: 41.051 pesos// Percentil 95% más alto proyectado: 46.315

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26284.61 26323.83
Lo.80 26417.00 26488.31
Point.Forecast 26668.91 26799.01
Hi.80 31482.21 32162.56
Hi.95 34372.51 35001.85


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,2) 
## 
## Coefficients:
##           ma1      ma2
##       -0.5517  -0.2958
## s.e.   0.1178   0.1321
## 
## sigma^2 = 37898:  log likelihood = -494.63
## AIC=995.27   AICc=995.61   BIC=1002.18
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1  intercept     xreg
##       0.3686   591.0263  14.1117
## s.e.  0.0989   262.2242   8.0260
## 
## sigma^2 = 36262:  log likelihood = -498.66
## AIC=1005.31   AICc=1005.89   BIC=1014.58
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 678.4072 646.2154 701.4202
Lo.80 816.0921 790.9497 796.5527
Point.Forecast 1076.1852 1064.3594 1012.6735
Hi.80 1336.2783 1363.0738 1287.0884
Hi.95 1473.9633 1521.2037 1461.1317


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.4.0        ggiraph_0.8.13     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.0      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.3  data.table_1.17.0  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.0.2   
##   [4] httr2_1.1.2         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.17.0      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.2.0       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.10         rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.2       askpass_1.2.1       pkgbuild_1.4.7     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       data.tree_1.1.0     tokenizers_0.3.0   
##  [31] listenv_0.9.1       anytime_0.3.11      performance_0.13.0 
##  [34] spatial_7.3-17      parallelly_1.43.0   codetools_0.2-20   
##  [37] xml2_1.3.8          tidyselect_1.2.1    ggeffects_2.2.1    
##  [40] farver_2.1.2        urca_1.3-4          its.analysis_1.6.0 
##  [43] matrixStats_1.5.0   stats4_4.4.0        jsonlite_2.0.0     
##  [46] ellipsis_0.3.2      Formula_1.2-5       iterators_1.0.14   
##  [49] systemfonts_1.2.2   foreach_1.5.2       tools_4.4.0        
##  [52] glue_1.8.0          xfun_0.52           TTR_0.24.4         
##  [55] ggfortify_0.4.17    loo_2.8.0           withr_3.0.2        
##  [58] timeSeries_4041.111 fastmap_1.2.0       boot_1.3-30        
##  [61] openssl_2.3.2       caTools_1.18.3      digest_0.6.37      
##  [64] timechange_0.3.0    R6_2.6.1            lfe_3.1.1          
##  [67] colorspace_2.1-1    networkD3_0.4.1     gtools_3.9.5       
##  [70] generics_0.1.3      htmlwidgets_1.6.4   ggstats_0.9.0      
##  [73] pkgconfig_2.0.3     gtable_0.3.6        timeDate_4041.110  
##  [76] lmtest_0.9-40       selectr_0.4-2       janeaustenr_1.0.0  
##  [79] htmltools_0.5.8.1   carData_3.0-5       tseries_0.10-58    
##  [82] snakecase_0.11.1    knitr_1.50          rstudioapi_0.17.1  
##  [85] tzdb_0.5.0          uuid_1.2-1          nlme_3.1-164       
##  [88] curl_6.2.2          cachem_1.1.0        sjlabelled_1.2.0   
##  [91] KernSmooth_2.23-22  parallel_4.4.0      fBasics_4041.97    
##  [94] pillar_1.10.2       vctrs_0.6.5         gplots_3.2.0       
##  [97] slam_0.1-55         car_3.1-3           dbplyr_2.5.0       
## [100] xtable_1.8-4        evaluate_1.0.3      mvtnorm_1.3-3      
## [103] cli_3.6.5           compiler_4.4.0      crayon_1.5.3       
## [106] rngtools_1.5.2      future.apply_1.11.3 labeling_0.4.3     
## [109] sjmisc_2.8.10       rstan_2.32.7        QuickJSR_1.7.0     
## [112] viridisLite_0.4.2   assertthat_0.2.1    lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.40.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.27     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))